Map-less and camera-based lane markings sampling method for level-3 autonomous driving vehicles

ABSTRACT

A computer-implemented method, apparatus, and system for discretizing lane markings and for generating a lane reference line is disclosed. A polynomial defined over an (x,y) coordinate system is received, the polynomial being representative of at least a portion of a lane boundary line. A length of the polynomial is determined. The polynomial is discretized, comprising determining a plurality of discretization points on the polynomial to represent the polynomial, wherein a first discretization point is a first end of the polynomial, wherein subsequent discretization points are determined successively until the polynomial is completely discretized, and wherein each discretization point other than the first discretization point is determined based at least in part on a slope of the polynomial at a previous discretization point. Thereafter, a lane reference line comprising a plurality of points is generated based on the discretized polynomial.

CROSS-REFERENCE TO RELATED APPLICATION

This patent application is a U.S. National Phase Application under 35U.S.C. § 371 of International Application No. PCT/CN2019/080098, filedMar. 28, 2019, entitled “A MAP-LESS AND CAMERA-BASED LANE MARKINGSSAMPLING METHOD FOR LEVEL-3 AUTONOMOUS DRIVING VEHICLES,” which isincorporated by reference herein by its entirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to operatingautonomous vehicles. More particularly, embodiments of the disclosurerelate to lane marking sampling for Level 3 autonomous driving.

BACKGROUND

Vehicles operating in an autonomous mode (e.g., driverless) can relieveoccupants, especially the driver, from some driving-relatedresponsibilities. When operating in an autonomous mode, the vehicle cannavigate to various locations using onboard sensors, allowing thevehicle to travel with minimal human interaction or in some caseswithout any passengers.

For a map-less and camera-based Level 3 autonomous vehicle, lane markingdetection is often carried out by cameras. For instance, aforward-facing camera detects a lane marking ahead, which is representedas polynomials (usually cubic polynomials). In order to process thosepolynomials properly and to generate a reference line for motionplanning, the polynomials need to be discretized.

SUMMARY

In a first aspect, the present disclosure provides acomputer-implemented method for operating an autonomous driving vehicle,the method comprising: generating a polynomial representing at least aportion of a lane boundary line of a lane in which an autonomous drivingvehicle (ADV) is moving; discretizing the polynomial into a plurality ofdiscretization points along the polynomial, including determining afirst discretization point disposed on a first end of the polynomial,and successively determining subsequent discretization points along thepolynomial, wherein each subsequent discretization point is determinedbased at least in part on a slope of the polynomial at a previousdiscretization point; and generating a lane reference line based on thediscretization points of the polynomial, wherein the lane reference lineis utilized to plan a trajectory to drive the ADV within the lane.

In a second aspect, the present disclosure provides a non-transitorymachine-readable medium having instructions stored therein, which whenexecuted by a processor, cause the processor to perform operations, theoperations comprising: generating a polynomial representing at least aportion of a lane boundary line of a lane in which an autonomous drivingvehicle (ADV) is moving; discretizing the polynomial into a plurality ofdiscretization points along the polynomial, including determining afirst discretization point disposed on a first end of the polynomial,and successively determining subsequent discretization points along thepolynomial, wherein each subsequent discretization point is determinedbased at least in part on a slope of the polynomial at a previousdiscretization point; and generating a lane reference line based on thediscretization points of the polynomial, wherein the lane reference lineis utilized to plan a trajectory to drive the ADV within the lane.

In a third aspect, the present disclosure provides a data processingsystem, comprising: a processor; and a memory coupled to the processorto store instructions, which when executed by the processor, cause theprocessor to perform operations, the operations including: generating apolynomial representing at least a portion of a lane boundary line of alane in which an autonomous driving vehicle (ADV) is moving,discretizing the polynomial into a plurality of discretization pointsalong the polynomial, including determining a first discretization pointdisposed on a first end of the polynomial, and successively determiningsubsequent discretization points along the polynomial, wherein eachsubsequent discretization point is determined based at least in part ona slope of the polynomial at a previous discretization point, andgenerating a lane reference line based on the discretization points ofthe polynomial, wherein the lane reference line is utilized to plan atrajectory to drive the ADV within the lane.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the disclosure are illustrated by way of example and notlimitation in the figures of the accompanying drawings in which likereferences indicate similar elements.

FIG. 1 is a block diagram illustrating a networked system according toone embodiment.

FIG. 2 is a block diagram illustrating an example of an autonomousvehicle according to one embodiment.

FIGS. 3A-3B are block diagrams illustrating an example of a perceptionand planning system used with an autonomous vehicle according to oneembodiment.

FIG. 4 is a block diagram illustrating various components utilizedaccording to one embodiment.

FIG. 5 is a diagram illustrating discretization of a polynomialaccording to one embodiment.

FIG. 6 is a diagram illustrating generation of a lane reference lineaccording to one embodiment.

FIG. 7 is a flowchart illustrating an example method for discretizing alane marking polynomial according to one embodiment.

FIG. 8 is a flowchart illustrating an example method for discretizing alane marking polynomial according to another embodiment.

FIG. 9 is a block diagram illustrating a data processing systemaccording to one embodiment.

DETAILED DESCRIPTION

Various embodiments and aspects of the disclosures will be describedwith reference to details discussed below, and the accompanying drawingswill illustrate the various embodiments. The following description anddrawings are illustrative of the disclosure and are not to be construedas limiting the disclosure. Numerous specific details are described toprovide a thorough understanding of various embodiments of the presentdisclosure. However, in certain instances, well-known or conventionaldetails are not described in order to provide a concise discussion ofembodiments of the present disclosures.

Reference in the specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin conjunction with the embodiment can be included in at least oneembodiment of the disclosure. The appearances of the phrase “in oneembodiment” in various places in the specification do not necessarilyall refer to the same embodiment.

One embodiment of the disclosure relates to a computer-implementedmethod, apparatus, and system for discretizing lane markings and forgenerating a lane reference line to autonomously drive an autonomousdriving vehicle (ADV). A polynomial is obtained representing at least aportion of a lane boundary line of a lane in which the ADV is driving.In one embodiment, the polynomial has been generated based on cameradetection of lane markings, without having to use map data associatedwith the lane. The polynomial is then discretized by sampling somereference points along the polynomial into a number of discretizedpoints. In discretizing the polynomial, the first overall point isselected, for example, at the starting of the polynomial. The subsequentpoints are sampled or selected based on at least in part on slope itsimmediate or adjacent preceding point.

The subsequent points are determined for the entire polynomial. In oneembodiment, the length of the polynomial is determined using aGauss-Legendre integration method. In one embodiment, the discretizationpoints are further apart along the polynomial where the slope of thepolynomial is smaller, and vice versa. It should be appreciated thatselecting the discretization points in this way enables appropriatesampling of the lane markings across various conditions, includingstraight as well as curved lane markings.

In one embodiment, for each subsequent discretization point i other thanthe first discretization point, its x-coordinate x_(i) is determinedbased on a formula x_(i)=x_(i−1)+1/√{square root over (1+d(x_(i−1))²)},where x_(i−1) is an x-coordinate of a previous discretization point, andd(x_(i−1)) represents a slope of the polynomial at the previousdiscretization point. The slope can be calculated based on a derivativeof the polynomial at the point. It should be appreciated that for thepolynomial f(x)=ax³+bx²+cx+d, the slope at a given point (x_(i−1),y_(i−1)) is d(x_(i−1))=3ax_(i−1) ²+2bx_(i−1)+c. And the y-coordinatey_(i) for the discretization point i is determined based on itsx-coordinate x_(i) and the polynomial (i.e., y_(i)=ax_(i) ³+bx_(i)²+cx_(i)+d). Thereafter, a lane reference line is generated based on thediscretized points of the polynomial. In one embodiment, the lanereference line is generated based on two discretized polynomialsrepresentative of both lane boundary lines on both sides of the lane.

FIG. 1 is a block diagram illustrating an autonomous vehicle networkconfiguration according to one embodiment of the disclosure. Referringto FIG. 1, network configuration 100 includes autonomous vehicle 101that may be communicatively coupled to one or more servers 103-104 overa network 102. Although there is one autonomous vehicle shown, multipleautonomous vehicles can be coupled to each other and/or coupled toservers 103-104 over network 102. Network 102 may be any type ofnetworks such as a local area network (LAN), a wide area network (WAN)such as the Internet, a cellular network, a satellite network, or acombination thereof, wired or wireless. Server(s) 103-104 may be anykind of servers or a cluster of servers, such as Web or cloud servers,application servers, backend servers, or a combination thereof. Servers103-104 may be data analytics servers, content servers, trafficinformation servers, map and point of interest (MPOI) servers, orlocation servers, etc.

An autonomous vehicle refers to a vehicle that can be configured to inan autonomous mode in which the vehicle navigates through an environmentwith little or no input from a driver. Such an autonomous vehicle caninclude a sensor system having one or more sensors that are configuredto detect information about the environment in which the vehicleoperates. The vehicle and its associated controller(s) use the detectedinformation to navigate through the environment. Autonomous vehicle 101can operate in a manual mode, a full autonomous mode, or a partialautonomous mode.

In one embodiment, autonomous vehicle 101 includes, but is not limitedto, perception and planning system 110, vehicle control system 111,wireless communication system 112, user interface system 113, and sensorsystem 115. Autonomous vehicle 101 may further include certain commoncomponents included in ordinary vehicles, such as, an engine, wheels,steering wheel, transmission, etc., which may be controlled by vehiclecontrol system 111 and/or perception and planning system 110 using avariety of communication signals and/or commands, such as, for example,acceleration signals or commands, deceleration signals or commands,steering signals or commands, braking signals or commands, etc.

Components 110-115 may be communicatively coupled to each other via aninterconnect, a bus, a network, or a combination thereof. For example,components 110-115 may be communicatively coupled to each other via acontroller area network (CAN) bus. A CAN bus is a vehicle bus standarddesigned to allow microcontrollers and devices to communicate with eachother in applications without a host computer. It is a message-basedprotocol, designed originally for multiplex electrical wiring withinautomobiles, but is also used in many other contexts.

Referring now to FIG. 2, in one embodiment, sensor system 115 includes,but it is not limited to, one or more cameras 211, global positioningsystem (GPS) unit 212, inertial measurement unit (IMU) 213, radar unit214, and a light detection and range (LIDAR) unit 215. GPS system 212may include a transceiver operable to provide information regarding theposition of the autonomous vehicle. IMU unit 213 may sense position andorientation changes of the autonomous vehicle based on inertialacceleration. Radar unit 214 may represent a system that utilizes radiosignals to sense objects within the local environment of the autonomousvehicle. In some embodiments, in addition to sensing objects, radar unit214 may additionally sense the speed and/or heading of the objects.LIDAR unit 215 may sense objects in the environment in which theautonomous vehicle is located using lasers. LIDAR unit 215 could includeone or more laser sources, a laser scanner, and one or more detectors,among other system components. Cameras 211 may include one or moredevices to capture images of the environment surrounding the autonomousvehicle. Cameras 211 may be still cameras and/or video cameras. A cameramay be mechanically movable, for example, by mounting the camera on arotating and/or tilting a platform.

Sensor system 115 may further include other sensors, such as, a sonarsensor, an infrared sensor, a steering sensor, a throttle sensor, abraking sensor, and an audio sensor (e.g., microphone). An audio sensormay be configured to capture sound from the environment surrounding theautonomous vehicle. A steering sensor may be configured to sense thesteering angle of a steering wheel, wheels of the vehicle, or acombination thereof. A throttle sensor and a braking sensor sense thethrottle position and braking position of the vehicle, respectively. Insome situations, a throttle sensor and a braking sensor may beintegrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system 111 includes, but is notlimited to, steering unit 201, throttle unit 202 (also referred to as anacceleration unit), and braking unit 203. Steering unit 201 is to adjustthe direction or heading of the vehicle. Throttle unit 202 is to controlthe speed of the motor or engine that in turn controls the speed andacceleration of the vehicle. Braking unit 203 is to decelerate thevehicle by providing friction to slow the wheels or tires of thevehicle. Note that the components as shown in FIG. 2 may be implementedin hardware, software, or a combination thereof.

Referring back to FIG. 1, wireless communication system 112 is to allowcommunication between autonomous vehicle 101 and external systems, suchas devices, sensors, other vehicles, etc. For example, wirelesscommunication system 112 can wirelessly communicate with one or moredevices directly or via a communication network, such as servers 103-104over network 102. Wireless communication system 112 can use any cellularcommunication network or a wireless local area network (WLAN), e.g.,using WiFi to communicate with another component or system. Wirelesscommunication system 112 could communicate directly with a device (e.g.,a mobile device of a passenger, a display device, a speaker withinvehicle 101), for example, using an infrared link, Bluetooth, etc. Userinterface system 113 may be part of peripheral devices implementedwithin vehicle 101 including, for example, a keyboard, a touch screendisplay device, a microphone, and a speaker, etc.

Some or all of the functions of autonomous vehicle 101 may be controlledor managed by perception and planning system 110, especially whenoperating in an autonomous driving mode. Perception and planning system110 includes the necessary hardware (e.g., processor(s), memory,storage) and software (e.g., operating system, planning and routingprograms) to receive information from sensor system 115, control system111, wireless communication system 112, and/or user interface system113, process the received information, plan a route or path from astarting point to a destination point, and then drive vehicle 101 basedon the planning and control information. Alternatively, perception andplanning system 110 may be integrated with vehicle control system 111.

For example, a user as a passenger may specify a starting location and adestination of a trip, for example, via a user interface. Perception andplanning system 110 obtains the trip related data. For example,perception and planning system 110 may obtain location and routeinformation from an MPOI server, which may be a part of servers 103-104.The location server provides location services and the MPOI serverprovides map services and the POIs of certain locations. Alternatively,such location and MPOI information may be cached locally in a persistentstorage device of perception and planning system 110.

While autonomous vehicle 101 is moving along the route, perception andplanning system 110 may also obtain real-time traffic information from atraffic information system or server (TIS). Note that servers 103-104may be operated by a third party entity. Alternatively, thefunctionalities of servers 103-104 may be integrated with perception andplanning system 110. Based on the real-time traffic information, MPOIinformation, and location information, as well as real-time localenvironment data detected or sensed by sensor system 115 (e.g.,obstacles, objects, nearby vehicles), perception and planning system 110can plan an optimal route and drive vehicle 101, for example, viacontrol system 111, according to the planned route to reach thespecified destination safely and efficiently.

Server 103 may be a data analytics system to perform data analyticsservices for a variety of clients. In one embodiment, data analyticssystem 103 includes data collector 121 and machine learning engine 122.Data collector 121 collects driving statistics 123 from a variety ofvehicles, either autonomous vehicles or regular vehicles driven by humandrivers. Driving statistics 123 include information indicating thedriving commands (e.g., throttle, brake, steering commands) issued andresponses of the vehicles (e.g., speeds, accelerations, decelerations,directions) captured by sensors of the vehicles at different points intime. Driving statistics 123 may further include information describingthe driving environments at different points in time, such as, forexample, routes (including starting and destination locations), MPOIs,road conditions, weather conditions, etc.

Based on driving statistics 123, machine learning engine 122 generatesor trains a set of rules, algorithms, and/or predictive models 124 for avariety of purposes. In one embodiment, algorithms 124 may includealgorithms for processing lane markings based on camera data. Algorithms124 can then be uploaded on ADVs to be utilized during autonomousdriving in real-time.

FIGS. 3A and 3B are block diagrams illustrating an example of aperception and planning system used with an autonomous vehicle accordingto one embodiment. System 300 may be implemented as a part of autonomousvehicle 101 of FIG. 1 including, but is not limited to, perception andplanning system 110, control system 111, and sensor system 115.Referring to FIGS. 3A-3B, perception and planning system 110 includes,but is not limited to, localization module 301, perception module 302,prediction module 303, decision module 304, planning module 305, controlmodule 306, routing module 307, and polynomial discretization module308.

Some or all of modules 301-308 may be implemented in software, hardware,or a combination thereof. For example, these modules may be installed inpersistent storage device 352, loaded into memory 351, and executed byone or more processors (not shown). Note that some or all of thesemodules may be communicatively coupled to or integrated with some or allmodules of vehicle control system 111 of FIG. 2. Some of modules 301-308may be integrated together as an integrated module.

Localization module 301 determines a current location of autonomousvehicle 300 (e.g., leveraging GPS unit 212) and manages any data relatedto a trip or route of a user. Localization module 301 (also referred toas a map and route module) manages any data related to a trip or routeof a user. A user may log in and specify a starting location and adestination of a trip, for example, via a user interface. Localizationmodule 301 communicates with other components of autonomous vehicle 300,such as map and route information 311, to obtain the trip related data.For example, localization module 301 may obtain location and routeinformation from a location server and a map and POI (MPOI) server. Alocation server provides location services and an MPOI server providesmap services and the POIs of certain locations, which may be cached aspart of map and route information 311. While autonomous vehicle 300 ismoving along the route, localization module 301 may also obtainreal-time traffic information from a traffic information system orserver.

Based on the sensor data provided by sensor system 115 and localizationinformation obtained by localization module 301, a perception of thesurrounding environment is determined by perception module 302. Theperception information may represent what an ordinary driver wouldperceive surrounding a vehicle in which the driver is driving. Theperception can include the lane configuration, traffic light signals, arelative position of another vehicle, a pedestrian, a building,crosswalk, or other traffic related signs (e.g., stop signs, yieldsigns), etc., for example, in a form of an object. The laneconfiguration includes information describing a lane or lanes, such as,for example, a shape of the lane (e.g., straight or curvature), a widthof the lane, how many lanes in a road, one-way or two-way lane, mergingor splitting lanes, exiting lane, etc.

Perception module 302 may include a computer vision system orfunctionalities of a computer vision system to process and analyzeimages captured by one or more cameras in order to identify objectsand/or features in the environment of autonomous vehicle. The objectscan include traffic signals, road way boundaries, other vehicles,pedestrians, and/or obstacles, etc. The computer vision system may usean object recognition algorithm, video tracking, and other computervision techniques. In some embodiments, the computer vision system canmap an environment, track objects, and estimate the speed of objects,etc. Perception module 302 can also detect objects based on othersensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module 303 predicts what the objectwill behave under the circumstances. The prediction is performed basedon the perception data perceiving the driving environment at the pointin time in view of a set of map/rout information 311 and traffic rules312. For example, if the object is a vehicle at an opposing directionand the current driving environment includes an intersection, predictionmodule 303 will predict whether the vehicle will likely move straightforward or make a turn. If the perception data indicates that theintersection has no traffic light, prediction module 303 may predictthat the vehicle may have to fully stop prior to enter the intersection.If the perception data indicates that the vehicle is currently at aleft-turn only lane or a right-turn only lane, prediction module 303 maypredict that the vehicle will more likely make a left turn or right turnrespectively.

For each of the objects, decision module 304 makes a decision regardinghow to handle the object. For example, for a particular object (e.g.,another vehicle in a crossing route) as well as its metadata describingthe object (e.g., a speed, direction, turning angle), decision module304 decides how to encounter the object (e.g., overtake, yield, stop,pass). Decision module 304 may make such decisions according to a set ofrules such as traffic rules or driving rules 312, which may be stored inpersistent storage device 352.

Routing module 307 is configured to provide one or more routes or pathsfrom a starting point to a destination point. For a given trip from astart location to a destination location, for example, received from auser, routing module 307 obtains route and map information 311 anddetermines all possible routes or paths from the starting location toreach the destination location. Routing module 307 may generate areference line in a form of a topographic map for each of the routes itdetermines from the starting location to reach the destination location.A reference line refers to an ideal route or path without anyinterference from others such as other vehicles, obstacles, or trafficcondition. That is, if there is no other vehicle, pedestrians, orobstacles on the road, an ADV should exactly or closely follows thereference line. The topographic maps are then provided to decisionmodule 304 and/or planning module 305. Decision module 304 and/orplanning module 305 examine all of the possible routes to select andmodify one of the most optimal routes in view of other data provided byother modules such as traffic conditions from localization module 301,driving environment perceived by perception module 302, and trafficcondition predicted by prediction module 303. The actual path or routefor controlling the ADV may be close to or different from the referenceline provided by routing module 307 dependent upon the specific drivingenvironment at the point in time.

Based on a decision for each of the objects perceived, planning module305 plans a path or route for the autonomous vehicle, as well as drivingparameters (e.g., distance, speed, and/or turning angle), using areference line provided by routing module 307 as a basis. That is, for agiven object, decision module 304 decides what to do with the object,while planning module 305 determines how to do it. For example, for agiven object, decision module 304 may decide to pass the object, whileplanning module 305 may determine whether to pass on the left side orright side of the object. Planning and control data is generated byplanning module 305 including information describing how vehicle 300would move in a next moving cycle (e.g., next route/path segment). Forexample, the planning and control data may instruct vehicle 300 to move10 meters at a speed of 30 mile per hour (mph), then change to a rightlane at the speed of 25 mph.

Based on the planning and control data, control module 306 controls anddrives the autonomous vehicle, by sending proper commands or signals tovehicle control system 111, according to a route or path defined by theplanning and control data. The planning and control data includesufficient information to drive the vehicle from a first point to asecond point of a route or path using appropriate vehicle settings ordriving parameters (e.g., throttle, braking, steering commands) atdifferent points in time along the path or route.

In one embodiment, the planning phase is performed in a number ofplanning cycles, also referred to as driving cycles, such as, forexample, in every time interval of 100 milliseconds (ms). For each ofthe planning cycles or driving cycles, one or more control commands willbe issued based on the planning and control data. That is, for every 100ms, planning module 305 plans a next route segment or path segment, forexample, including a target position and the time required for the ADVto reach the target position. Alternatively, planning module 305 mayfurther specify the specific speed, direction, and/or steering angle,etc. In one embodiment, planning module 305 plans a route segment orpath segment for the next predetermined period of time such as 5seconds. For each planning cycle, planning module 305 plans a targetposition for the current cycle (e.g., next 5 seconds) based on a targetposition planned in a previous cycle. Control module 306 then generatesone or more control commands (e.g., throttle, brake, steering controlcommands) based on the planning and control data of the current cycle.

Note that decision module 304 and planning module 305 may be integratedas an integrated module. Decision module 304/planning module 305 mayinclude a navigation system or functionalities of a navigation system todetermine a driving path for the autonomous vehicle. For example, thenavigation system may determine a series of speeds and directionalheadings to affect movement of the autonomous vehicle along a path thatsubstantially avoids perceived obstacles while generally advancing theautonomous vehicle along a roadway-based path leading to an ultimatedestination. The destination may be set according to user inputs viauser interface system 113. The navigation system may update the drivingpath dynamically while the autonomous vehicle is in operation. Thenavigation system can incorporate data from a GPS system and one or moremaps so as to determine the driving path for the autonomous vehicle.

FIG. 4 is a block diagram 400 illustrating various components utilizedaccording to one embodiment. Camera 211 captures the lane markings(e.g., lane boundary lines in the front of the autonomous vehicle). Thecamera 211 may include or be coupled to a lane marking detection module(which may be implemented as a part of perception module 302), whichgenerates polynomials representative of the lane boundary lines. Theboundary line polynomials are input into the polynomial discretizationmodule 308, which discretizes the boundary line polynomials. Thediscretized boundary line(s) are forwarded to a reference linegeneration module 430, where the lane reference line is generated. Notethat the polynomial discretization module 308 may be integrated as apart of reference line generation module 430. Reference line generationmodule 430 may also be integrated as a part of routing module 307.

In one embodiment, perception module 302 generates a polynomialrepresenting at least a portion of a lane boundary line of a lane inwhich the ADV is driving, based on camera data obtained from a camera.In one embodiment, the polynomial has been generated based on cameradetection of lane markings. In one embodiment, the polynomial is a cubicpolynomial (i.e., the polynomial may be in a form of f(x)=ax³+bx²+cx+d).A length of the polynomial is determined, which may determined using aGauss-Legendre integration method.

The polynomial is then discretized by polynomial discretization module308 according to the length of the polynomial. The discretizing of thepolynomial includes determining a plurality of discretization points onthe polynomial to represent the polynomial. A first discretization pointmay be determined at a first end of the polynomial (e.g., starting pointof the polynomial). Subsequent discretization points may be determinedsuccessively until the polynomial is completely discretized. Eachsuccessive discretization point is determined based at least in part ona slope of the polynomial at a previous discretization point (e.g., animmediately or adjacent preceding point). In one embodiment, thediscretization points are further apart along the polynomial if theslope of the polynomial is smaller, and vice versa. It should beappreciated that selecting the discretization points in this way enablesappropriate sampling of the lane markings across various conditions,including straight as well as curved lane markings.

In one embodiment, for each subsequent discretization point i other thanthe first discretization point, its x-coordinate x_(i) is determinedbased on a formula x_(i)=x_(i−1)+1/√{square root over (1+d(x_(i−1))²)},where x_(i−1) is an x-coordinate of a previous discretization point, andd(x_(i−1)) is a slope of the polynomial at the previous discretizationpoint. The slope can be calculated based on a derivative of thepolynomial at the point. It should be appreciated that for thepolynomial of f(x)=ax³+bx²+cx+d, the slope at a given point (x_(i−1),y_(i−1)) is d(x_(i−1))=3ax_(i−1) ²+2bx_(i−1)+c. The y-coordinate y_(i)for the discretization point i is determined based on its x-coordinatex_(i) and the polynomial (i.e., y_(i)=ax_(i) ³+bx_(i) ²+cx_(i)+d).Thereafter, a lane reference line is generated by reference linegeneration module 430 based on the discretized points of the polynomial.In one embodiment, the lane reference line is generated based on twodiscretized polynomials representative of both lane boundary lines onboth sides of the lane. The reference line is then utilized by planningmodule 305 to plan a trajectory to drive the vehicle to drive along thelane.

FIG. 5 is a diagram 500 illustrating discretization of a polynomialaccording to one embodiment. A lane boundary line polynomial 510 is tobe discretized into discretization points 520A-520G using at least someof the techniques described above. In other words, a plurality ofdiscretization points 520A-520G on the polynomial 510 are determined torepresent the polynomial 510. The discretization step size is inverselycorrelated with the sloped of the polynomial 510. In other words, thediscretization points 520A-520G are further apart along the polynomial510 where the slope of the polynomial 510 is smaller, and vice versa. Itshould be appreciated that selecting the discretization points 520A-520Gin this way enables appropriate sampling of the lane markings acrossvarious conditions, including straight as well as curved lane markings.

FIG. 6 is a diagram 600 illustrating generation of a lane reference lineaccording to one embodiment. A lane reference line 620 is generatedbased on the lane boundary line 610 (which can be discretized asdescribed above). A lane reference line may be close to a center line ofthe lane. Generation of the lane reference line 620 segments thatcorrespond to segments ab and bc (a−b−c) of the lane boundary line 610is described for illustrative purposes. A segment a₁b_(a) is generatedbased on the segment ab, where segments a₁b_(a) and ab are parallel toeach other, the length of the segment alba is equal to the length of thesegment ab, the distance between the segments a₁b_(a) and ab is half thelane width, and the segment a₁b_(a) is perpendicular to a line thatconnects b and b_(a). Similarly, a segment b_(c)c₁ is generated based onthe segment bc, where segments b_(c)c₁ and bc are parallel to eachother, the length of the segment b_(c)c₁ is equal to the length of thesegment bc, the distance between the segments b_(c)c₁ and bc is half thelane width, and the segment b_(c)c₁ is perpendicular to a line thatconnects b and b_(c). Further, b₁ is the point of intersection betweensegments a₁b_(a) and b_(c)c₁. Thus, segments a₁−b₁−c₁ represent theportion of the lane reference line 620 that corresponds to the segmentsa−b−c on the lane boundary line 610. It should be appreciated that theconstructed segments a₁−b₁−c₁ possess the following characteristic: thecurvature of the segments a₁−b₁−c₁ C(a₁b₁c₁) is the curvature of thesegments a−b−c C(abc) minus half the road width d, that is,C(a₁b₁c₁)=C(abc)−d. This characteristic ensures the reference lineconstructed from boundary line(s) preserves the shape of the road.

Another reference line comprising segments a₂−b₂−c₂ can be constructedfrom the other boundary line on the other side of the lane. In a perfectsituation, segments a₁−b₁−c₁ and a₂−b₂−c₂ should coincide. However, dueto sensor noises and the imperfection of the roadway, the two referencelines generated respectively from the two lane boundary lines almostnever completely match. In one embodiment, a weighted reference line maybe determined: e.g., a_(w)−b_(w)−c_(w)=w*(a₁−b₁−c₁)+(1−w)*(a₂−b₂−c₂).The weight w can be adjusted empirically according to different sensorsystems. In one embodiment, the weight w may be 0.5.

FIG. 7 is a flowchart illustrating an example method 700 fordiscretizing a lane marking polynomial and generating a lane referenceline according to one embodiment. Method 700 may be performed byprocessing logic which may include software, hardware, or a combinationthereof. For example, method 700 may be performed by system 400 of FIG.4. Referring to FIG. 7, at block 701, processing logic generates apolynomial representing at least a portion of a lane boundary line of alane in which an ADV is moving. The polynomial may be generated based onimage data obtained from one or more cameras mounted on the ADVcapturing lane markings of the lane, without having to use map dataassociated with the lane. At block 702, processing logic discretizes thepolynomial into a number of discretized points along the polynomial,including selecting a first overall point, for example, at the start ofthe polynomial. At block 703, processing iteratively samples subsequentpoints after the first overall point. Each of the subsequent points isselected based on the slope of the immediate preceding point, forexample, using a predetermined formula as described above. Thereafter,at block 704, a lane reference line is generated based on thediscretized points of the polynomial. The reference line is utilized inplanning a trajectory to drive the ADV to navigate within the lane.

FIG. 8 is a flow diagram illustrating a process of discretizing apolynomial according to one embodiment. Process 800 may be performed byprocessing logic which may include software, hardware, and a combinationthereof. Referring to FIG. 8, in response to a polynomial generatedbased on camera data, at block 801, the length of the polynomial isdetermined, for example, using a Gauss Legendre integration method. Atblock 802, a first discretized point is selected, for example, at thebeginning of the polynomial and the slope of the first point iscalculated, for example, based on a derivative of the first point. Foreach of the subsequent points, an iterative process is performed. Forexample, given a cubic polynomial f(x)=ax³+bx²+cx+d, the slope at thefirst point (x₀, y₀) can be calculated as d(x₀)=3ax₀ ²+2bx₀+c. Withinthe iteration, at block 803, the coordinates of a current point (x₁, y₁)of the polynomial is determined based on the coordinates and the slopeof a preceding point of a prior iteration. For example, according to oneembodiment, x₁=x₀+1/√{square root over (1+d(x₀)²)} and y₁=ax₁ ³+bx₁²+cx₁+d. For the initial current point (x₁, y₁), the preceding point isthe first overall point (x₀, y₀) determined at block 802. Similarly,x₂=x₁+1/√{square root over (1+d(x₁)²)}, and y₂=ax₂ ³+bx₂ ²+cx₂+d, and soon. At block 804, a slope of the current point is calculated. At block805, it is determined whether there are more points to be processed, forexample, based on the length of the polynomial determined at block 801.If so, at block 806, the current point is designated as the precedingpoint and a next point becomes the current point, and the aboveiterative process in blocks 803-804 is repeatedly performed until theentire polynomial has been processed.

Note that some or all of the components as shown and described above maybe implemented in software, hardware, or a combination thereof. Forexample, such components can be implemented as software installed andstored in a persistent storage device, which can be loaded and executedin a memory by a processor (not shown) to carry out the processes oroperations described throughout this application. Alternatively, suchcomponents can be implemented as executable code programmed or embeddedinto dedicated hardware such as an integrated circuit (e.g., anapplication specific IC or ASIC), a digital signal processor (DSP), or afield programmable gate array (FPGA), which can be accessed via acorresponding driver and/or operating system from an application.Furthermore, such components can be implemented as specific hardwarelogic in a processor or processor core as part of an instruction setaccessible by a software component via one or more specificinstructions.

FIG. 9 is a block diagram illustrating an example of a data processingsystem which may be used with one embodiment of the disclosure. Forexample, system 1500 may represent any of data processing systemsdescribed above performing any of the processes or methods describedabove, such as, for example, perception and planning system 110 or anyof servers 103-104 of FIG. 1. System 1500 can include many differentcomponents. These components can be implemented as integrated circuits(ICs), portions thereof, discrete electronic devices, or other modulesadapted to a circuit board such as a motherboard or add-in card of thecomputer system, or as components otherwise incorporated within achassis of the computer system.

Note also that system 1500 is intended to show a high level view of manycomponents of the computer system. However, it is to be understood thatadditional components may be present in certain implementations andfurthermore, different arrangement of the components shown may occur inother implementations. System 1500 may represent a desktop, a laptop, atablet, a server, a mobile phone, a media player, a personal digitalassistant (PDA), a Smartwatch, a personal communicator, a gaming device,a network router or hub, a wireless access point (AP) or repeater, aset-top box, or a combination thereof. Further, while only a singlemachine or system is illustrated, the term “machine” or “system” shallalso be taken to include any collection of machines or systems thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein.

In one embodiment, system 1500 includes processor 1501, memory 1503, anddevices 1505-1508 connected via a bus or an interconnect 1510. Processor1501 may represent a single processor or multiple processors with asingle processor core or multiple processor cores included therein.Processor 1501 may represent one or more general-purpose processors suchas a microprocessor, a central processing unit (CPU), or the like. Moreparticularly, processor 1501 may be a complex instruction set computing(CISC) microprocessor, reduced instruction set computing (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processor 1501 may alsobe one or more special-purpose processors such as an applicationspecific integrated circuit (ASIC), a cellular or baseband processor, afield programmable gate array (FPGA), a digital signal processor (DSP),a network processor, a graphics processor, a communications processor, acryptographic processor, a co-processor, an embedded processor, or anyother type of logic capable of processing instructions.

Processor 1501, which may be a low power multi-core processor socketsuch as an ultra-low voltage processor, may act as a main processingunit and central hub for communication with the various components ofthe system. Such processor can be implemented as a system on chip (SoC).Processor 1501 is configured to execute instructions for performing theoperations and steps discussed herein. System 1500 may further include agraphics interface that communicates with optional graphics subsystem1504, which may include a display controller, a graphics processor,and/or a display device.

Processor 1501 may communicate with memory 1503, which in one embodimentcan be implemented via multiple memory devices to provide for a givenamount of system memory. Memory 1503 may include one or more volatilestorage (or memory) devices such as random access memory (RAM), dynamicRAM (DRAM), synchronous DRAM (SDRAM), static RAM (SRAM), or other typesof storage devices. Memory 1503 may store information includingsequences of instructions that are executed by processor 1501, or anyother device. For example, executable code and/or data of a variety ofoperating systems, device drivers, firmware (e.g., input output basicsystem or BIOS), and/or applications can be loaded in memory 1503 andexecuted by processor 1501. An operating system can be any kind ofoperating systems, such as, for example, Robot Operating System (ROS),Windows operating system from Microsoft®, Mac OS®/iOS® from Apple,Android® from Google®, LINUX, UNIX, or other real-time or embeddedoperating systems.

System 1500 may further include IO devices such as devices 1505-1508,including network interface device(s) 1505, optional input device(s)1506, and other optional IO device(s) 1507. Network interface device1505 may include a wireless transceiver and/or a network interface card(NIC). The wireless transceiver may be a WiFi transceiver, an infraredtransceiver, a Bluetooth transceiver, a WiMax transceiver, a wirelesscellular telephony transceiver, a satellite transceiver (e.g., a globalpositioning system (GPS) transceiver), or other radio frequency (RF)transceivers, or a combination thereof. The NIC may be an Ethernet card.

Input device(s) 1506 may include a mouse, a touch pad, a touch sensitivescreen (which may be integrated with display device 1504), a pointerdevice such as a stylus, and/or a keyboard (e.g., physical keyboard or avirtual keyboard displayed as part of a touch sensitive screen). Forexample, input device 1506 may include a touch screen controller coupledto a touch screen. The touch screen and touch screen controller can, forexample, detect contact and movement or break thereof using any of aplurality of touch sensitivity technologies, including but not limitedto capacitive, resistive, infrared, and surface acoustic wavetechnologies, as well as other proximity sensor arrays or other elementsfor determining one or more points of contact with the touch screen.

IO devices 1507 may include an audio device. An audio device may includea speaker and/or a microphone to facilitate voice-enabled functions,such as voice recognition, voice replication, digital recording, and/ortelephony functions. Other IO devices 1507 may further include universalserial bus (USB) port(s), parallel port(s), serial port(s), a printer, anetwork interface, a bus bridge (e.g., a PCI-PCI bridge), sensor(s)(e.g., a motion sensor such as an accelerometer, gyroscope, amagnetometer, a light sensor, compass, a proximity sensor, etc.), or acombination thereof. Devices 1507 may further include an imagingprocessing subsystem (e.g., a camera), which may include an opticalsensor, such as a charged coupled device (CCD) or a complementarymetal-oxide semiconductor (CMOS) optical sensor, utilized to facilitatecamera functions, such as recording photographs and video clips. Certainsensors may be coupled to interconnect 1510 via a sensor hub (notshown), while other devices such as a keyboard or thermal sensor may becontrolled by an embedded controller (not shown), dependent upon thespecific configuration or design of system 1500.

To provide for persistent storage of information such as data,applications, one or more operating systems and so forth, a mass storage(not shown) may also couple to processor 1501. In various embodiments,to enable a thinner and lighter system design as well as to improvesystem responsiveness, this mass storage may be implemented via a solidstate device (SSD). However in other embodiments, the mass storage mayprimarily be implemented using a hard disk drive (HDD) with a smalleramount of SSD storage to act as a SSD cache to enable non-volatilestorage of context state and other such information during power downevents so that a fast power up can occur on re-initiation of systemactivities. Also a flash device may be coupled to processor 1501, e.g.,via a serial peripheral interface (SPI). This flash device may providefor non-volatile storage of system software, including BIOS as well asother firmware of the system.

Storage device 1508 may include computer-accessible storage medium 1509(also known as a machine-readable storage medium or a computer-readablemedium) on which is stored one or more sets of instructions or software(e.g., module, unit, and/or logic 1528) embodying any one or more of themethodologies or functions described herein. Processingmodule/unit/logic 1528 may represent any of the components describedabove, such as, for example, planning module 305, control module 306,polynomial discretization module 308. Processing module/unit/logic 1528may also reside, completely or at least partially, within memory 1503and/or within processor 1501 during execution thereof by data processingsystem 1500, memory 1503 and processor 1501 also constitutingmachine-accessible storage media. Processing module/unit/logic 1528 mayfurther be transmitted or received over a network via network interfacedevice 1505.

Computer-readable storage medium 1509 may also be used to store the somesoftware functionalities described above persistently. Whilecomputer-readable storage medium 1509 is shown in an exemplaryembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database, and/or associated cachesand servers) that store the one or more sets of instructions. The terms“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing or encoding a set of instructions forexecution by the machine and that cause the machine to perform any oneor more of the methodologies of the present disclosure. The term“computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, and optical andmagnetic media, or any other non-transitory machine-readable medium.

Processing module/unit/logic 1528, components and other featuresdescribed herein can be implemented as discrete hardware components orintegrated in the functionality of hardware components such as ASICS,FPGAs, DSPs or similar devices. In addition, processingmodule/unit/logic 1528 can be implemented as firmware or functionalcircuitry within hardware devices. Further, processing module/unit/logic1528 can be implemented in any combination hardware devices and softwarecomponents.

Note that while system 1500 is illustrated with various components of adata processing system, it is not intended to represent any particulararchitecture or manner of interconnecting the components; as suchdetails are not germane to embodiments of the present disclosure. Itwill also be appreciated that network computers, handheld computers,mobile phones, servers, and/or other data processing systems which havefewer components or perhaps more components may also be used withembodiments of the disclosure.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as those set forth in the claims below, refer to the actionand processes of a computer system, or similar electronic computingdevice, that manipulates and transforms data represented as physical(electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. Such a computer program is stored in anon-transitory computer readable medium. A machine-readable mediumincludes any mechanism for storing information in a form readable by amachine (e.g., a computer). For example, a machine-readable (e.g.,computer-readable) medium includes a machine (e.g., a computer) readablestorage medium (e.g., read only memory (“ROM”), random access memory(“RAM”), magnetic disk storage media, optical storage media, flashmemory devices).

The processes or methods depicted in the preceding figures may beperformed by processing logic that comprises hardware (e.g. circuitry,dedicated logic, etc.), software (e.g., embodied on a non-transitorycomputer readable medium), or a combination of both. Although theprocesses or methods are described above in terms of some sequentialoperations, it should be appreciated that some of the operationsdescribed may be performed in a different order. Moreover, someoperations may be performed in parallel rather than sequentially.

Embodiments of the present disclosure are not described with referenceto any particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof embodiments of the disclosure as described herein.

In the foregoing specification, embodiments of the disclosure have beendescribed with reference to specific exemplary embodiments thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of the disclosure as setforth in the following claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense.

What is claimed is:
 1. A computer-implemented method for operating anautonomous driving vehicle, the method comprising: generating apolynomial representing at least a portion of a lane boundary line of alane in which an autonomous driving vehicle (ADV) is moving;discretizing the polynomial into a plurality of discretization pointsalong the polynomial, including determining a first discretization pointdisposed on a first end of the polynomial, and successively determiningsubsequent discretization points along the polynomial, wherein eachsubsequent discretization point is determined based at least in part ona slope of the polynomial at a previous discretization point, whereinfor each successive discretization point i, its x-coordinate x_(i) isdetermined based on a formula of x_(i)=x_(i−1)+1/√{square root over(1+d(x_(i−1))²)}, where x_(i−1) is an x-coordinate of a previousdiscretization point, and d(x_(i−1)) is a slope of the polynomial at theprevious discretization point; and generating a lane reference linebased on the discretization points of the polynomial, wherein the lanereference line is utilized to control a trajectory to drive the ADVwithin the lane.
 2. The method of claim 1, wherein the polynomial isgenerated based on camera data obtained from a camera mounted on the ADVcapturing the lane without using map data corresponding to the lane. 3.The method of claim 1, wherein the polynomial is a cubic polynomial. 4.The method of claim 1, further comprising determining a length of thepolynomial using a Gauss-Legendre integration method, wherein thesubsequent discretization points are successively determined based onthe length of the polynomial.
 5. The method of claim 1, wherein thediscretization points are further apart along the polynomial where theslope of the polynomial is smaller, and vice versa.
 6. The method ofclaim 1, wherein a y-coordinate y_(i) for each successive discretizationpoint i is determined based on its x-coordinate x_(i) and a formula ofy_(i)=ax_(i) ³+bx_(i) ²+cx_(i)+d where a, b, c, and d are constants. 7.The method of claim 1, wherein for the successive discretization pointi, its y-coordinate y_(i) is determined based on its x-coordinate x_(i)and the polynomial.
 8. The method of claim 1, wherein each subsequentdiscretization point is determined, in part, by calculating a slope ofthe polynomial at the previous discretization point and using thecalculated slope to determine a location on the polynomial for thesubsequent discretization point.
 9. A non-transitory machine-readablemedium having instructions stored therein, which when executed by aprocessor, cause the processor to perform operations, the operationscomprising: generating a polynomial representing at least a portion of alane boundary line of a lane in which an autonomous driving vehicle(ADV) is moving; discretizing the polynomial into a plurality ofdiscretization points along the polynomial, including determining afirst discretization point disposed on a first end of the polynomial,and successively determining subsequent discretization points along thepolynomial, wherein each subsequent discretization point is determinedbased at least in part on a slope of the polynomial at a previousdiscretization point, wherein for each successive discretization pointi, its x-coordinate x_(i) is determined based on a formula ofx_(i)=x_(i−1)+1/√{square root over (1+d(x_(i−1))²)}, where x_(i−1) is anx-coordinate of a previous discretization point, and d(x_(i−1)) is aslope of the polynomial at the previous discretization point; andgenerating a lane reference line based on the discretization points ofthe polynomial, wherein the lane reference line is utilized to control atrajectory to drive the ADV within the lane.
 10. The machine-readablemedium of claim 9, wherein the polynomial is generated based on cameradata obtained from a camera mounted on the ADV capturing the lanewithout using map data corresponding to the lane.
 11. Themachine-readable medium of claim 9, wherein the polynomial is a cubicpolynomial.
 12. The machine-readable medium of claim 9, wherein theoperations further comprise determining a length of the polynomial usinga Gauss-Legendre integration method, wherein the subsequentdiscretization points are successively determined based on the length ofthe polynomial.
 13. The machine-readable medium of claim 9, wherein thediscretization points are further apart along the polynomial where theslope of the polynomial is smaller, and vice versa.
 14. Themachine-readable medium of claim 9, wherein a y-coordinate y_(i) foreach successive discretization point i is determined based on itsx-coordinate x_(i) and a formula of y_(i)=ax_(i) ³+bx_(i) ²+cx_(i)+dwhere a, b, c, and d are constants.
 15. The machine-readable medium ofclaim 9, wherein for the successive discretization point i, itsy-coordinate y_(i) is determined based on its x-coordinate x_(i) and thepolynomial.
 16. The machine-readable medium of claim 9, wherein eachsubsequent discretization point is determined, in part, by calculating aslope of the polynomial at the previous discretization point and usingthe calculated slope to determine a location on the polynomial for thesubsequent discretization point.
 17. A data processing system,comprising: a processor; and a memory coupled to the processor to storeinstructions, which when executed by the processor, cause the processorto perform operations, the operations including generating a polynomialrepresenting at least a portion of a lane boundary line of a lane inwhich an autonomous driving vehicle (ADV) is moving, discretizing thepolynomial into a plurality of discretization points along thepolynomial, including determining a first discretization point disposedon a first end of the polynomial, and successively determiningsubsequent discretization points along the polynomial, wherein eachsubsequent discretization point is determined based at least in part ona slope of the polynomial at a previous discretization point, whereinfor each successive discretization point i, its x-coordinate x_(i) isdetermined based on a formula of x_(i)=x_(i−1)+1/√{square root over(1+d(x_(i−1))²)}, where x_(i−1) is an x-coordinate of a previousdiscretization point, and d(x_(i−1)) is a slope of the polynomial at theprevious discretization point, and generating a lane reference linebased on the discretization points of the polynomial, wherein the lanereference line is utilized to control a trajectory to drive the ADVwithin the lane.
 18. The system of claim 17, wherein the polynomial isgenerated based on camera data obtained from a camera mounted on the ADVcapturing the lane without using map data corresponding to the lane. 19.The system of claim 17, wherein the polynomial is a cubic polynomial.20. The system of claim 17, wherein each subsequent discretization pointis determined, in part, by calculating a slope of the polynomial at theprevious discretization point and using the calculated slope todetermine a location on the polynomial for the subsequent discretizationpoint.